Deep Learning based Automated Food Image Classification

Food image classification is essential for numerous applications, like nutritional analysis, food recording, and dietary evaluation. The proliferation of social media and smartphones generated a significant increase in the sharing of food photographs online, necessitating automated systems capable of accurately classifying food items in images. Convolutional Neural Networks (CNNs), particularly the MobileNetV2 architecture, have emerged as extremely strong machinery for identifying image tasks because of its capacity to extract unique characteristics directly from raw pixel data. This work focuses on utilizing CNNs and the MobileNetV2 architecture for deep learning-based automatic food image classification. MobileNetV2 strikes a balance between classification accuracy and processing efficiency, making it suitable for mobile and embedded vision applications. The study addresses important challenges in food image classification, including intra-class variances, cluttered backgrounds, and disparities in food appearance. The objective aims to set up a reliable and effective system which can determine food products in various visual scenarios by leveraging state-of-the-art deep learning techniques.